Experiment tracking for
machine learning models
Experiment tracking
A system of record for your model results
Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard.
Fast integration
Set up your code in 5 minutes
Add a few lines to your script to start logging results. Our lightweight integration works with any Python script. See the docs →

import torch
import torch.nn as nn

import wandb

# Log any metric from your training script
wandb.log({"acc": accuracy, "val_acc": val_accuracy})

Accessible anywhere
Flexible project management on desktop and mobile
Check the latest training model and results on desktop and mobile. Use collaborative hosted project to coordinate across your team.
Visualize training
Optimize hyperparameters and compare results
See model metrics stream live into interactive graphs and tables. It is easy to see how your latest model is performing compared to previous experiments, no matter where you are training your models.
Reproducible models
Quickly find and re-run previous models
Save everything you need to reproduce models later— the latest git commit, hyperparameters, model weights, and even sample test predictions. You can save experiment files directly to W&B or store pointers to your own storage.
System metrics
CPU and GPU usage across runs
Visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources.
Model predictions
Debug performance in real time
Log model predictions to see how your model is performing, and identify problem areas during training. We support rich media including images, video, audio, and 3D objects.
Collaborative reports
Share high level updates and detailed work logs
It's never been easier to share updates with your coworkers. Explain how your model works, show graphs of how your model versions improved, discuss bugs, and demonstrate progress towards milestones.

See an example report →
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